---
model-index:
- name: Cloud Service RAG Model
  results:
  - task:
      type: text-generation
    dataset:
      name: rag_contents
      type: pricing_info
    metrics:
    - type: custom
      value: 85.0
      name: Retrieval Accuracy
  - task:
      type: retrieval-augmented generation (RAG)
    dataset:
      name: pricing_info
      type: custom
    metrics:
    - type: custom
      value: 75
      name: Pricing Retrieval Accuracy
---

# ☁️ Cloud Cents RAG Model ☁️

## 📖 Model Description
This model is a fine-tuned GPT-2 model designed to answer cloud-related questions about AWS, Azure, GCP, and other cloud platforms. It uses **Retrieval-Augmented Generation (RAG)** to combine document retrieval with text generation, leveraging cloud-related documents and real-time pricing information.

## 🛠 Intended Use
- 🔍 Cloud service comparisons (e.g., AWS vs Azure)
- 💰 Real-time cloud pricing queries
- 🌐 General information about cloud platforms and services

## 🔧 Pipeline
`text-generation` with `retrieval-augmented generation (RAG)`
- 📚 Document retrieval from `rag_contents` and `pricing_info`
- ✍️ Text generation using fine-tuned GPT-2

## 📊 Datasets
- **rag_contents**: Contains cloud-related documents from sources such as AWS, Azure, GCP.
- **pricing_info**: Provides real-time pricing details for cloud services (e.g., EC2, Blob Storage, Container Registry).

## 📈 Metrics
- **Perplexity**: Evaluated for fluency of text generation.
- **BLEU**: Used for measuring the accuracy of generated answers.
- **Retrieval Accuracy**: Custom metric for FAISS-based document retrieval.

## ⚠️ Limitations
- 🕑 The model may not always retrieve the most up-to-date information about cloud services.
- 📂 The retrieval and generation quality is based on the documents stored in the `rag_contents` table.